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Machine Learning Estimates of Global Marine Nitrogen Fixation

Publication ,  Journal Article
Tang, W; Li, Z; Cassar, N
Published in: Journal of Geophysical Research: Biogeosciences
March 1, 2019

Marine nitrogen (N2) fixation supplies “new” nitrogen to the global ocean, supporting uptake and sequestration of carbon. Despite its central role, marine N2 fixation and its controlling factors remain elusive. In this study, we compile over 1,100 published observations to identify the dominant predictors of marine N2 fixation and derive global estimates based on the machine learning algorithms of random forest and support vector regression. We find that no single environmental property predicts N2 fixation at global scales. Our random forest and support vector regression algorithms, trained with sampling coordinates and month, solar radiation, wind speed, sea surface temperature, sea surface salinity, surface nitrate, surface phosphate, surface excess phosphorus, minimum oxygen in upper 500 m, photosynthetically available radiation, mixed layer depth, averaged photosynthetically available radiation in the mixed layer, and chlorophyll-a concentration, estimate global marine N2 fixation ranging from 68 to 90 Tg N/year. Comparison of our machine learning estimates and 11 other model outputs currently available in literature shows substantial discrepancies in the global magnitude and spatial distribution of marine N2 fixation, especially in the tropics and in high latitudes. The large uncertainties in marine N2 fixation highlighted in our study argue for increased and more coordinated efforts using geochemical tracers, modeling, and observations over broad ocean regions.

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Published In

Journal of Geophysical Research: Biogeosciences

DOI

EISSN

2169-8961

ISSN

2169-8953

Publication Date

March 1, 2019

Volume

124

Issue

3

Start / End Page

717 / 730

Related Subject Headings

  • 3706 Geophysics
  • 0404 Geophysics
 

Citation

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Tang, W., Li, Z., & Cassar, N. (2019). Machine Learning Estimates of Global Marine Nitrogen Fixation. Journal of Geophysical Research: Biogeosciences, 124(3), 717–730. https://doi.org/10.1029/2018JG004828
Tang, W., Z. Li, and N. Cassar. “Machine Learning Estimates of Global Marine Nitrogen Fixation.” Journal of Geophysical Research: Biogeosciences 124, no. 3 (March 1, 2019): 717–30. https://doi.org/10.1029/2018JG004828.
Tang W, Li Z, Cassar N. Machine Learning Estimates of Global Marine Nitrogen Fixation. Journal of Geophysical Research: Biogeosciences. 2019 Mar 1;124(3):717–30.
Tang, W., et al. “Machine Learning Estimates of Global Marine Nitrogen Fixation.” Journal of Geophysical Research: Biogeosciences, vol. 124, no. 3, Mar. 2019, pp. 717–30. Scopus, doi:10.1029/2018JG004828.
Tang W, Li Z, Cassar N. Machine Learning Estimates of Global Marine Nitrogen Fixation. Journal of Geophysical Research: Biogeosciences. 2019 Mar 1;124(3):717–730.

Published In

Journal of Geophysical Research: Biogeosciences

DOI

EISSN

2169-8961

ISSN

2169-8953

Publication Date

March 1, 2019

Volume

124

Issue

3

Start / End Page

717 / 730

Related Subject Headings

  • 3706 Geophysics
  • 0404 Geophysics